Introduction to Partial Least Squares Discriminant Analysis PLS DA for beginners

In this webinar, graduate student Edwin Caballero offers an introduction on what is partial least squares discriminant analysis (PLS-DA)

PLS-DA is a model that classifies unknown samples in predetermined groups or classes for multivariate data (more than 3 variables).

----------------------------------------------------

CHAPTERS
00:00:00 Intro
00:04:50 How PLS-R Works?
00:13:14 How does PLS classify?
00:25:45 What algorithm does it use?
00:35:49 What do I need and what do I get for PLS-DA?
00:43:19 What are the SIMCA outputs?
00:53:39 How do we evaluate PLS-DA results?
01:05:20 Real-life examples of PLS-DA in use
01:07:43 Where can I use PLS-DA?

----------------------------------------------------

More information from the presenter
👉 https://www.uprm.edu/ccs-cicsa/dt_tea...

Want to know more of our group? Visit our webpage!
👉 https://www.uprm.edu/ccs-cicsa/

See our webinar/events
👉 https://www.uprm.edu/ccs-cicsa/news/

See our projects
👉 https://www.uprm.edu/ccs-cicsa/our-work/

See our facilities
👉 https://www.uprm.edu/ccs-cicsa/facili...

Partial least squares discriminant analysis (PLS-DA) is a statistical method that can be used to analyze and classify complex data sets. PLS-DA combines the techniques of partial least squares regression and linear discriminant analysis, and is often used in the fields of chemometrics and bioinformatics. PLS-DA can be used to identify patterns and relationships in data, and to classify objects or samples based on their characteristics. It is particularly useful for analyzing data with many variables, as it can handle large, multi-dimensional data sets and deal with issues of collinearity. PLS-DA can also be used to visualize data, allowing users to see patterns and trends more easily. Overall, PLS-DA is a powerful tool for analyzing and classifying complex data sets.

----------------------------------------------------

#PLS-DA #chemometrics #multivariate #statistics #research #college #chemistry #introduction #analysis #webinar